A Web-Based Training Intervention for Primary Care Providers on Preparing Patients for Cancer Treatment Decisions and Conversations About Clinical Trials: Evaluation of a Pilot Study Using Mixed Methods and Follow-Up
Notice bibliographique
Résumé
BACKGROUND: Recruitment to cancer clinical trials (CCTs) is low, particularly for underrepresented groups such as uninsured patients, those with low-income status, and racial and ethnic minoritized individuals. A significant barrier is that treating oncologists often fail to inform patients about the possibility of CCT participation as an option for quality cancer care. Therefore, patient inquiries about trials before starting treatment should be normalized and encouraged, particularly for underrepresented groups. Primary care providers (PCPs) are uniquely suited to do this because they interact with patients at the time of cancer diagnosis, provide ongoing care, and are trusted sources of information. OBJECTIVE: This study was designed to pilot an innovative web-based CCT training intervention for PCPs, including practicing clinicians and trainees, to increase their ability to prepare patients for cancer treatment decisions and conversations with oncologists about clinical trials. METHODS: We conducted an evaluation of a pilot study using a self-guided, 1-hour web-based training intervention for PCPs with survey assessments before the intervention, immediately after the intervention, and at the 3-month follow-up. We used a mixed methods approach, incorporating quantitative and qualitative data collection and analysis. The evaluation was guided by the Kirkpatrick evaluation model, focusing on levels 1 (reaction), 2 (learning), and 3 (behavior). RESULTS: A total of 29 PCPs completed the intervention and pre- and postintervention measures, with 28 (97%) PCPs completing the 3-month follow-up assessment. Of these 28 PCPs, 8 (29%) participated in a qualitative interview after the 3-month follow-up assessment. Participants reported high levels of satisfaction with the course. CCT knowledge, as well as attitudes and beliefs, improved after the course and were sustained at the 3-month follow-up. PCPs reported willingness to communicate with patients about cancer treatment options, including CCTs, and willingness to talk with their colleagues about potential changes in referral practices. However, fewer PCPs had actually engaged in these conversations by the 3-month follow-up. In the interviews, PCPs cited limited interprofessional knowledge sharing and organizational constraints as barriers. Notably, PCPs reported changes in their communication behavior with patients: a higher proportion reported communicating with patients at the time of referral about cancer treatment options and clinical trials at the 3-month follow-up than at baseline. In the interviews, PCPs reported that they felt more comfortable and empowered to have these conversations. CONCLUSIONS: This pilot study found that a self-guided, 1-hour web-based training intervention for PCPs resulted in improved knowledge, attitudes, and beliefs, as well as improved communication with patients, to prepare them for discussions with oncologists about cancer treatment and CCTs. Future dissemination of this course has the potential to make an impact on CCT accrual.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,028 | 0,034 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,002 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,003 | 0,002 |
| Communication savante | 0,001 | 0,002 |
| Science ouverte | 0,003 | 0,003 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».